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Built and shipped a clean, data-driven frontend using Lovable
From dashboard analytics to seamless user flows, this project reflects my focus on intuitive UI, performance, and real-world problem solving.
Excited to keep building smarter, faster interfaces.
Assignment from Rauf ur Rahim
#FrontendDevelopment #Lovable #WebDesign #BuildInPublic
Asiya Akhtar and 7 others reacted
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🎓 I Just Built an AI Tutor That Works 168 Hours Per Week (and costs $0.002 per student) HACKATHON IV
Not a chatbot. Not a course platform. A Digital Full-Time Equivalent (FTE) educational tutor.
⚡ What it does:
- Tutors unlimited students simultaneously
- Available 24/7/365 (never sleeps, never gets tired)
- Works in ChatGPT App + Web App
- Explains concepts at each student's level
- Grades quizzes and tracks progress
- Costs 99% less than human tutors
🧠 The Secret: Zero-Backend-LLM Architecture
ChatGPT does ALL the teaching → Backend only serves content
Cost: $0.002 per user (vs $0.50-$2.00 traditional)
📊 The Math:
Human Tutor:
- $2,000-5,000/month
- 40 hours/week
- 20-50 students max
- $25-100 per session
Digital FTE:
- $200-500/month
- 168 hours/week
- Unlimited students
- $0.10-0.50 per session
99.5% cost reduction ✅
🎯 Dual Frontend Strategy:
📱 ChatGPT App:
- Reaches 800M+ ChatGPT users
- Conversational learning interface
- Zero marginal cost (users pay OpenAI, not me)
🌐 Web App:
- Full LMS dashboard (Next.js)
- Progress visualization
- Admin features
- Rich media support
🛠️ Built with:
- Next.js + React (Web frontend)
- FastAPI (Deterministic backend)
- Cloudflare R2 (Content storage)
- Agent Skills (Procedural knowledge)
🎓 Agent Skills Teach the FTE:
✓ concept-explainer - Adjusts complexity to student level
✓ socratic-tutor - Guides through questions, not answers
✓ quiz-master - Tests with encouragement
✓ progress-motivator - Celebrates achievements
📈 Results:
✅ 168 hrs/week availability (vs 40 for humans)
✅ Unlimited concurrent students
✅ 99%+ consistency
✅ 50+ languages
✅ Instant feedback
✅ $16-41/month for 10K users
💡 The Key Insight:
Most AI products fail on economics because they call LLM APIs for everything. The Zero-Backend-LLM pattern means ChatGPT (already paid for by users) does the intelligence, while your backend just serves content.
This is the difference between profitable and unprofitable AI products.
🔮 This is the future:
- Every course will have an AI tutor
- Education costs will drop 100x
- Quality will become consistent globally
- Human teachers will focus on mentorship, not repetitive explanations
The technology is ready. The infrastructure exists. The cost is negligible.
We're democratizing education one Digital FTE at a time.
What subjects or skills would you want a 24/7 AI tutor for? 👇
#AI #EdTech #Education #DigitalFTE #AITutor #MachineLearning #ChatGPT #OnlineLearning #ZeroBackendLLM #HackathonIV #LearningAndDevelopment #PersonalizedLearning #FutureOfEducation
sir Ali Jawwad sir Hafiz Ali Ahmed
🔗 Built in 48 hours following Agent Factory Architecture
💻 Dual frontend: ChatGPT App + Web App
📊 Cost: $0.002/user → makes freemium sustainable
Noor Mudasir and 5 others reacted
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🚀 𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧 5 — 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞!❤️
4+ 𝒉𝒐𝒖𝒓𝒔. 1,209 𝒍𝒊𝒏𝒆𝒔 𝒐𝒇 𝒎𝒂𝒏𝒖𝒂𝒍 𝒕𝒆𝒔𝒕 𝒅𝒐𝒄𝒖𝒎𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏. 𝑪𝒐𝒖𝒏𝒕𝒍𝒆𝒔𝒔 𝒃𝒖𝒈𝒔.
𝐀𝐮𝐫 𝐩𝐡𝐢𝐫 𝐰𝐨𝐡 𝐦𝐨𝐦𝐞𝐧𝐭... 𝐣𝐚𝐛 𝐥𝐚𝐬𝐭 𝐭𝐞𝐬𝐭 𝐜𝐡𝐚𝐥𝐚𝐲𝐚 𝐚𝐮𝐫 𝐬𝐜𝐫𝐞𝐞𝐧 𝐩𝐞 𝐚𝐚𝐲𝐚 —
✅ 𝐏𝐀𝐒𝐒𝐄𝐃🎉 Happy moment:)
𝐖𝐡𝐚𝐭 𝐰𝐚𝐬 𝐇𝐚𝐜𝐤𝐚𝐭𝐡𝐨𝐧 5 𝐚𝐛𝐨𝐮𝐭?
Building a 𝑭𝒖𝒍𝒍 𝑻𝒊𝒎𝒆 𝑬𝒒𝒖𝒊𝒗𝒂𝒍𝒆𝒏𝒕 (𝑭𝑻𝑬) 𝑨𝑰 𝑨𝒈𝒆𝒏𝒕 — the core idea being: how do you take a General Agent and architect it into Specialized Agents that can handle real customer success workflows autonomously?
The stack involved:
𝐅𝐚𝐬𝐭𝐀𝐏𝐈 𝐬𝐞𝐫𝐯𝐞𝐫 𝐰𝐢𝐭𝐡 𝐊𝐚𝐟𝐤𝐚 message queue for async processing
𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 (𝐍𝐞𝐨𝐧) with pgvector for semantic knowledge base search
𝐖𝐡𝐚𝐭𝐬𝐀𝐩𝐩 & 𝐆𝐦𝐚𝐢𝐥 webhook integrations
𝐖𝐞𝐛 𝐅𝐨𝐫𝐦 Integration
Full end-to-end agent pipeline: 𝐇𝐓𝐓𝐏 → 𝐊𝐚𝐟𝐤𝐚 → 𝐖𝐨𝐫𝐤𝐞𝐫 → 𝐀𝐠𝐞𝐧𝐭 → 𝐃𝐁
𝑻𝒉𝒆 𝒓𝒆𝒂𝒍 𝒕𝒂𝒌𝒆𝒂𝒘𝒂𝒚 𝒇𝒓𝒐𝒎 𝒕𝒐𝒅𝒂𝒚:
Implementing with AI assistance has become relatively straightforward. The harder problem is validating what was built — making sure the system actually behaves correctly under real conditions, not just in theory.
AI will confidently tell you "𝐁𝐨𝐬𝐬! 𝐀𝐥𝐥 𝐆𝐨𝐨𝐝" — bagier test kiye. That's the trap.
Manual testing isn't optional when you're building agentic systems. The failure modes are subtle, the edge cases are non-obvious, and the only way to trust the output is to systematically verify every layer of the stack — environment, database, API, message queue, agent tools, and webhooks — one by one.
That's exactly what this 1,209-𝐥𝐢𝐧𝐞 test guide was built for.
Will share the full project once it's live.
#AIEngineering #AgenticAI #MultiAgentSystems #CustomerSuccess #BuildInPublic #PakistaniDevelopers
Faqeha Noor and 11 others reacted
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Fatima Tul Zahra and 11 other connections follow GameBot
🌐 The Next Evolution: AI Societies Inside Games
At GameBot, we’ve built 𝗞𝗻𝗶𝘁 — a 𝐧𝐨-𝐜𝐨𝐝𝐞, 𝐋𝐋𝐌-𝐛𝐚𝐬𝐞𝐝 AI game agent development platform that scales beyond individual NPCs.
Today, over 𝟯 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 agents run concurrently across multiple titles — each learning, adapting, and interacting in real-time.
This isn’t just AI filling gameplay roles. It’s the rise of 𝗔𝗜 𝘀𝗼𝗰𝗶𝗲𝘁𝗶𝗲𝘀 — where agents form relationships, evolve behaviors, and grow alongside players.
With Knit, even non-technical designers can build living AI worlds, no front-end support required.
✨ The shift from scripted interactions to emergent intelligence has begun, are you ready?
#Knit #AI #Gaming #AINPC #GameAI #AICompanions #EmergentGameplay #GameBot
Talha Lodhi and 1,719 others reacted
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AI is no longer limited to autocomplete. It is actively shaping how frontend applications are designed, built, and scaled.
In this article, I explore:
What AI-Driven Development actually means
The rise of “Vibe Coding” in modern frontend workflows
Why AI accelerates syntax but not architecture or UX
The critical difference between building with AI and building for AI
A practical, engineering-focused perspective on where frontend development is heading.
#AIDrivenDevelopment
#VibeCoding
#VibeCoder
#FrontendDevelopment
#ReactJS
#NextJS
#WebEngineering
#SoftwareArchitecture
#AIinTech
#UXEngineering
#DevProductivity
Ali VijdanIrfan AliAhmed Saleem
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Choosing the right programming language can shape your entire tech career.
When starting in tech, many people ask:
“Which programming language should I learn?”
The answer depends on your career path and the field you want to grow in.
Here’s a simple roadmap based on different domains:
💻 Web Development
Learn: JavaScript, Python, Java, PHP, C#, Ruby
🎮 Game Development
Learn: C++, C#, Java, Python
📊 Data Analysis / Data Science
Learn: Python, R, MATLAB, Java
🖥️ Desktop Application Development
Learn: Java, C#, C++, Python
⚙️ Embedded Systems / IoT
Learn: C, C++, Python
📱 Mobile App Development
Learn: Kotlin, Swift, Java, Dart, JavaScript, C#
One important lesson I’ve learned:
Don’t try to learn everything at once. Start with one language, build projects, and then expand based on your goals.
Currently, I’m focusing on strengthening my fundamentals and exploring technologies that align with Web Development, AI, and modern application development.
The journey in tech is all about continuous learning and practical experience.
Which programming language did you start your journey with?
#Programming #Coding #SoftwareDevelopment #WebDevelopment #Python #JavaScript #Java #CSharp #CPP #MobileDevelopment #DataScience #DeveloperJourney #LearningInPublic #TechCareer #FutureDeveloper #ComputerScience #TechCommunity #SkillDevelopment
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Marium Noor and 14 other connections follow Akamai Technologies
Strikes, smiles, and strong connections! Our Akamites in #AkamaiChina came together for a bowling event centered on wellness and team engagement 🎳
In a remote-first environment, creating moments to connect really matters. That’s why we continue to invest in in-person, virtual, and site-based experiences that bring our teams closer and keep everyone feeling like #OneAkamai 💙
Ali Naqi and 1,264 others reacted
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